Using sequence to sequence learning for digital BPSK and QPSK demodulation

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Kalade, Sarunas and Crockett, Louise and Stewart, Robert; (2018) Using sequence to sequence learning for digital BPSK and QPSK demodulation. In: IEEE 5G World Forum. IEEE, USA. (In Press)

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Abstract

In the last few years Machine Learning (ML) has seen explosive growth in a wide range of research fields and industries. With the advancements in Software Defined Radio (SDR), which allows more intelligent, adaptive radio systems to be built, the wireless communications field has a number of opportunities to apply ML techniques. In this paper, a novel approach to demodulation using a Sequence to Sequence (Seq2Seq) model is proposed. This type of model is shown to work effectively with PSK data and also has a number of useful properties that are not present in other machine learning algorithms. A basic Seq2Seq implementation for BPSK and QPSK demodulation is presented in this paper, and learned properties such as Automatic Modulation Classification (AMC), and ability to adapt to different length input sequences, are demonstrated. This is an exciting new avenue of research that provides considerable potential for application in next generation 5G networks.

ORCID iDs

Kalade, Sarunas ORCID logoORCID: https://orcid.org/0000-0001-5512-7402, Crockett, Louise ORCID logoORCID: https://orcid.org/0000-0003-4436-0254 and Stewart, Robert ORCID logoORCID: https://orcid.org/0000-0002-7779-8597;
  • Item type:Book Section
    ID code:63954
    Dates:
    Date
    Event
    30 April 2018
    Published
    30 April 2018
    Accepted
    Notes:© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:08 May 2018 13:45
    Last modified:18 Nov 2024 01:20
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/63954
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